2nd/10_Wiki/Topic_Programming/Architecture/Complex Event Processing (CEP).md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-complex-event-processing-cep	Complex Event Processing (CEP)	10_Wiki/Topics	verified	self	CEP Event Stream Processing 복합 이벤트 처리	none	A	0.88	applied	cep streaming event-driven flink esper		2026-05-10	pending	language framework java flink

Complex Event Processing (CEP)

매 한 줄

"매 stream of simple events → meaningful complex pattern". David Luckham (Stanford, 2002) 가 정의한 paradigm. 2026 현재 Apache Flink CEP, Kafka Streams, Esper NEsper 가 main implementation; fraud detection, IoT anomaly, algorithmic trading 의 backbone.

매 핵심

매 개념

• Event: timestamped 의 fact (transaction, sensor reading, click).
• Pattern: temporal/causal relationship 의 events (A followed by B within 5s).
• Window: sliding/tumbling/session 시간 frame.
• Aggregation: count, sum, avg over window.
• Correlation: 다중 stream 매 join (e.g., trades + market data).

매 pattern operator

• Sequence: A → B → C (in order).
• Conjunction: A AND B (any order, in window).
• Negation: A NOT followed by B.
• Iteration: A repeated N times.
• Within: temporal constraint.

매 응용

1. Fraud detection — card swipes 매 different countries within 1h.
2. IoT — sensor reading exceeds threshold for 3 consecutive readings.
3. Trading — bid/ask spread anomaly detection.
4. Network security — port scan pattern (many SYN, few ACK).
5. SLA monitoring — 5xx error rate spike correlated with deploy event.

💻 패턴

Flink CEP — 3 failed login pattern

Pattern<LoginEvent, ?> failedLogins = Pattern
    .<LoginEvent>begin("first")
    .where(e -> !e.success)
    .next("second").where(e -> !e.success)
    .next("third").where(e -> !e.success)
    .within(Time.minutes(5));

CEP.pattern(loginStream.keyBy(e -> e.userId), failedLogins)
   .select(match -> new Alert(match.get("first").get(0).userId))
   .addSink(alertSink);

Esper EPL — fraud detection

-- swipe in different countries within 1 hour
SELECT a.cardId, a.country, b.country
FROM pattern [
    every a=Swipe -> b=Swipe(cardId=a.cardId, country!=a.country)
        where timer:within(1 hour)
];

Kafka Streams — sliding window aggregation

KStream<String, Click> clicks = builder.stream("clicks");

clicks.groupByKey()
      .windowedBy(SlidingWindows.ofTimeDifferenceWithNoGrace(Duration.ofMinutes(5)))
      .count()
      .filter((k, count) -> count > 1000)
      .toStream()
      .to("anomalies");

Flink — session window

stream.keyBy(e -> e.userId)
      .window(EventTimeSessionWindows.withGap(Time.minutes(30)))
      .aggregate(new SessionStats())
      .addSink(...);

Pattern with negation (NO heartbeat in 30s)

Pattern.<HeartbeatEvent>begin("start")
    .notFollowedBy("missing")
    .where(e -> true)
    .within(Time.seconds(30));

Modern: Materialize / RisingWave (SQL-native streaming)

CREATE MATERIALIZED VIEW fraud_alerts AS
SELECT user_id, COUNT(*) as failed_count
FROM logins
WHERE success = false
  AND ts > NOW() - INTERVAL '5 minutes'
GROUP BY user_id
HAVING COUNT(*) >= 3;

매 결정 기준

상황	Approach
Java/JVM, complex patterns	Flink CEP
Kafka-centric, simple aggregation	Kafka Streams
SQL-first, low ops	Materialize / RisingWave
In-process, low-volume	Esper
Cloud-native, serverless	AWS Kinesis Data Analytics

기본값: Flink CEP for complex patterns, Materialize for SQL-native streaming.

🔗 Graph

• 부모: Event-Driven Architecture · Stream-Processing-Architectures
• 변형: Event Sourcing · CQRS
• Adjacent: Apache Flink

🤖 LLM 활용

언제: pattern definition 매 natural language → EPL/Flink translation, alert explanation. 언제 X: micro-second latency hot path (LLM 매 too slow).

❌ 안티패턴

• Unbounded state: window 없이 group-by → memory blowup.
• Wall-clock instead of event-time: out-of-order event 매 wrong result.
• Pattern explosion: NFA state count 매 exponential, pattern 너무 복잡.
• No watermark: late event 매 silently lost.

🧪 검증 / 중복

• Verified (Luckham 2002 Power of Events, Apache Flink CEP docs 2026).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full content with Flink CEP, Esper, Materialize